Quantitative MRI with Automated Histogram Analysis Based on Self-Supervised Learning of Organ Segmentation: Demonstration for Liver T1 Mapping

Motivation: Extensive recent work has been devoted to quantitative MRI, but practical implementation of quantitative parameter mapping is hindered by the lack of tools for easy visualization and automated analysis. Goal(s): We demonstrate the applicability of a self-supervised contrastive pretraining framework for organ segmentation in automated analysis of free-breathing 3D liver T1 mapping. Approach: A DL model is pretrained to learn T1 contrast information from multi-contrast images acquired for T1 parameter mapping. Results: With few labeled examples, an organ segmentation framework was developed, and its utility in interpreting parameter maps was demonstrated. Impact: Multi-contrast information from images typically acquired for parameter estimation in quantitative MRI can be leveraged to pretrain organ segmentation models with self-supervision, enabling automated analysis of quantitative parameter maps.